Humans of Martech 71: Find the top AI marketing tools and filter out the noise
What’s up everyone,
If you haven’t checked out our previous 3 episodes in our AI series you might want to before this episode, we give you a lot of context around some of the events that have happened and will shape the conversation today.
So basically
- How fast could AI change or replace marketing jobs?
- How marketers can stay informed and become AI fluent
- Exploring new paths to future-proof your marketing career in the age of AI
Today we’re diving into specific tools… there’s a lot of noise out there right now.
- What tools you should play around with
In TMW #107 | ChatGPT and the artificial marketer, Juan Mendoza explains that
“...generative AI tools are already everywhere. From text generation to video and audio production, to image creation, there’s a thriving industry of technologies taking small slices out of our creative talents, packaging them up, and selling them as a SaaS product on a recurring revenue model. If you’re wanting to stay relevant five years from now in the marketing technology industry, you’re probably going to have to learn some of these platforms. In 2010 we used to say: “there’s an app for that”. In 2023, we will be saying: “there’s an AI for that.””
Outline
Here are some of the topics for this third AI episode:
- Key AI technology definitions and how to differentiate real AI tools vs all the noise out there
- Deep dive into tools
- Content marketing tools
- Email and marketing automation tools
- Predictive analytics tools
- Text to presentation and pitch deck tools
- 3D animation tools for product marketers
- Sales and outreach tools
- Text to website creator tools
- Ad and social creative tools
- AutoGPT and AI agents
- And a bunch of other tools like conversational search engines, 1-1 convos with celebrities and an even longer list of honorable mentions
Here’s today’s main takeaway:
The key to future proofing your marketing career with the ever changing AI landscape is to stay curious, get your hands dirty and experiment fearlessly: Fill out some forms, spin up free trials, get on wait lists, and give new AI tools a chance. It's only by actually getting your hands dirty that you'll discover which tools truly work for you and which are just part of the ever growing sea of gimmicky AI tools.
Definition of tech terms
I’ll be using some of these terms throughout my analysis of some of these tools so here’s a primer explaining the three most common AI technologies used for marketing applications:
ML
Machine Learning): ML is a way to teach computers to learn by themselves, without having to be programmed for every task. They learn from examples and data patterns to make predictions or decisions. Applications include segmentation, predictive analytics and propensity models.
NLP
Natural Language Processing: NLP is a subset of ML and focuses on enabling computers to understand, interpret, and generate human language. Includes sentiment analysis, machine translation, named entity recognition, text summarization, and more. NLP techniques usually helps computers understand and communicate with humans using everyday language.
GNN
Graph Neural Network: GNN also a subset of ML is a type of neural network that aims to handle graph-structured data, data organized like a network or web of connected points. Applications include analyzing relationships between different things like users in a social network or users in your database or recommending additional products based on past purchase history.
Real AI vs noise
Part of the reason AI gets a really bad rep, especially in martech, is that anything that’s built on if statements or simple Javascript logic gets called AI. There’s still plenty of AI startups that shout about their proprietary AI when it’s probably just a few decision trees and a few interns running spreadsheets.
Now though, you have an even bigger bucket of noise that’s essentially “slight tweak on Chat-GPT”.
Developing AI that was comparable to human performance was a challenging feat prior to GPT's arrival. To achieve this level of sophistication, a company would have had to:
- make a substantial investment, amounting to millions of dollars
- developing its own algorithms
- performing extensive data cleanup
But it’s so easy now because GPT is so good out of the box.
Allen Cheng puts it simply. Starting a new AI venture can be achieved by simply assembling a few elements:
- a product developed on GPT-4's user-friendly API
- a website,
- and a marketing campaign.
This is why we’re seeing hundreds of AI tolls pop up every week.
A lot of these GPT-based products are pretty much indistinguishable from one another. Maybe a handful have a significant advantage over others but most are gimmicky. And over the next few months, every tool is going to be integrating ChatGPT features inside their products in the hopes of making it stickier.
The threat of GPT-n
The part that I find trickiest and the most discouraging about building anything on top of GPT is that any progress you make on fine tuning GPT-4 will totally be wiped out by GPT-5 or GPT-n… Kind of like we talked about in a previous episode with all the tools GPT’s plugins killed.
So let’s cut through the noise and dive into legit AI tools, the ones you should be playing with and experimenting.
Content marketing tools
Copy.ai and Jasper
AI text generators are very common these days, the two most popular tools, especially for marketers are Copy.ai and Jasper. Both allow you to bypass the initial stage of writing where you face a blank page.
The promise of these tools is that they help you in generating ideas, saving time on brainstorming and drafting, and ensuring a consistent production flow, freeing you to focus on higher-level strategic tasks, original research, and connecting with your audience.
I’ve played around with both Jasper and Copy.ai before ChatGPT came out… and they were super unique. But both Copy.ai and Jasper are built on top of GPT, they essentially rent usage of the platform. So they built a pretty nice UI on top of GPT… but now that ChatGPT came out, I’m sure they’ve seen a drop in usage. Plus GPT-4 is 3 times more expensive.
They still offer marketing specific value though and can get you up to speed faster than using CGPT in the form of templates, prompts and workflows. Both are super powerful, you could make a case that Jasper outshin...